Online search engine advertising is become an increasingly important piece of the marketing campaigns and sales strategies of many client businesses, or content providers. Often, the online search engine advertising is typically sold via keyword auctions (e.g., Google's AdWords, Yahoo's Search Marketing, and MSN's AdCenter). During these keyword auctions, prospective content providers choose a set of keywords relevant to their products, and for each keyword, each content provider submits a bid representing an estimate of utility for presenting an ad impression when that keyword is displayed. These keyword auctions to place the ad impression at locations on a web page have become the main source of revenue for many search engines, or online publishers, as well as a large expenditure for the content providers aspiring to post their ad impressions. Accordingly, analyzing the behavior of these auctions is critical to supporting content providers such that they enjoy a sufficient return-on-investment when engaging in online advertising.
In one instance, online publishers may support content providers by offering a price-estimation tool that attempts to approximate a price for posting an ad impression with the search engine. But, these price-estimation tools are flawed in several respects, and thus, provide inaccurate estimates of price to the content provider. Frequently, inaccurate estimates result from using data related to incumbent customers, which are content providers that have previously participated in a keyword auction. This incumbent-customer data is recycled within the price-estimation tool. Accordingly, when the content provider submits a proposal to the price-estimation tool, the content provider is caused to effectively compete against itself during an approximation of a price for posting the ad impression with the search engine.
By way of example, let there be two incumbent customers bidding for a highest position in association with a particular keyword: customer A with bid $0.20; and customer B with bid $0.10. Customer A's ad is placed at position 1, while customer B's ad is placed at position 2. Using second-bid style pricing, customer A will pay $0.10 per click on an ad impression while customer B will pay $0.05 per click, which is the minimal floor price. If customer B submits a proposal to attain position 1 for the particular keyword, the price-estimation engine should estimate the bid to be $0.21. This is the same result that should be calculated for any new customer. But, because the price-estimation tool is unable to distinguish the data related to the incumbent customer A, a proposal from customer A to attain position 1 will likely deliver a suggested bid of $0.21. A more accurate suggested bid would be $0.11, enough to overcome customer B, but not more. These flaws with the price-estimation tool are exaggerated when the stored bid for an incumbent customer at position 1 is substantially more than the stored bid associated with the next incumbent customer. This overestimation may directly lead to financial loss for online publishers. In particular, the incumbent customers have become increasingly unsatisfied when the actual costs for advertising at a search engine do not correspond to budgets created based on the inaccurate estimates.
Present techniques do not offer sufficient techniques for correcting bid estimates that are derived from data that include information related to participation of an incumbent customer in a keyword auction. Accordingly, implementing an algorithm to effectively discount bid data related to a content provider requesting a price estimate would uniquely increase the accuracy of a price-estimation tool and would enhance a content provider's experience when establishing an online advertising budget.